Hyperoxaluria is described by an augmented urinary elimination of oxalate. Systemic oxalosis is the term for the condition that occurs when the burden of calcium oxalate (CaOx) surpasses the renal capacity to excrete it. When individuals acquire chronic renal disease, elevated urinary oxalate levels aid in diagnosis, whereas plasma oxalate levels are probably more reliable. Based on bioinformatic analysis, the study aimed to identify differentially expressed genes (DEGs) and miRNA as potential biomarkers to differentiate normal versus hyperoxaluric states compared to the stage of CaOx crystals in the kidney. Published microarray data for gene expression patterns of normal controls, hyperoxaluric kidney tissue, and kidney tissue at the stage of crystal formation were collected from the National Center for Biotechnology Information Gene Expression Omnibus database. Integrated bioinformatics methods were utilized to analyze and compare these gene expression patterns. The data processing was conducted using R software. Gene ontology and the Kyoto Encyclopedia of Genes and Genomes database were employed to explore the enrichment of pathways and functions in the DEGs. Additionally, the STRING database was utilized to investigate protein–protein interactions. Tarbase, Mirnda, and DIANA software were used to obtain miRNAs for the top 10 DEGs. A total of 62,966 genes were screened, 2,814 were differentially expressed, out of which 603 genes were statistically significantly differentially expressed, after analyzing the GSE89028 dataset. A total of 2,810 genes were downregulated and only 4 genes were upregulated on day 14. The genes Cdt1 and cdhr4 were highly significantly differentiated with log2 (fold change) being −3.085 and −3.966, respectively, −log 10 (p-value) being 6.857 and 6.196, respectively, at 14 days. On day 28, 62,976 genes were screened, out of which 356 were significantly differentiated. Only four genes were upregulated and 240 genes were downregulated. Csmd1, Olr154, Cntfr, Zbtb16 log2 (fold change) being 1.188, 1.527, 1.782, and 2.636, respectively; −log 10 (p-value) being 4.071, 3.804, 4.357, and 4.061, respectively. The text mining evidence was observed on string analysis in both contexts. The strength of alternative splicing (cellular enrichment) was 1.16 with a false discovery rate of 0.0409. The study showcases the effectiveness of bioinformatics analytical methods in pinpointing potential pathogenic genes associated with hyperoxaluria and the deposition of crystals in the kidneys. The interaction network identified two miRNAs, hsa-miR-6884-5p and hsa-miR-4653-5p, and two genes CDHR4 and EGR2 as significant players.
Key words: hyperoxaluria, calcium oxalate crystals, kidney damage, bioinformatics, prediction of markers
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